The INGO I previously worked for developed a mobile app for multi-drug resistant tuberculosis (MDR-TB) contact screening and tracking to accelerate referral and project reporting for an MDR-TB patient care program. This MDRTB care program trains volunteers to provide DOT to new MDR-TB patients and other activities such as contact tracing, screening and referral, etc., and volunteers report back to health centers and the program using paper forms. This approach has a number of challenges, including: low surveillance of MDR-TB contacts and identifying the suspects, and inefficiencies in data collection, storage, retrieval and poor data quality. The purpose of the app is to help increase case detection and enrollment in treatment through improved screening, documentation, and referral practices. The app allows volunteers to go to an MDR-TB patient’s home to record the household contacts and their neighbors/close contacts. After entering an individual’s sociodemographic information, TB clinical symptoms, and risk factors, the app uses an algorithm to identify suspects or determine if they require other tests, such as a CXR or sputum testing.
The app has the potential to significantly improve MDR-TB contact screening and tracking in resource-limited settings. The program found significant improvement in contact trancing activity completion rates and referral rates, and also the reduction in reporting error. However, the development and implementation of the app presented a number of challenges. First, deciding on the rules of the algorithm was difficult since there are global and local criteria for identifying suspects, and these criteria are frequently updated. Second, training volunteers. A significant number of volunteers are unfamiliar with mobile apps and technologies, especially older volunteers with low education levels.